search for: permuate

Dear friends, I would like to get all possible arrangements of n objects listed 1:n on a circle. Now this is easy to do in R. Keep the last spot fixed at n and fill in the rest using permuations(n-1, n-1) from the gtools package. However, what if clockwise or counterclockwise arrangements are the same? I know that half of the above (n - 1)! arrangements are redundant. Is there an easy way to

I am trying to apply a permuation-based MANOVA (Anderson 2001) to a set of morphological data from three ecomorphs of fish reared under two different conditions and measured at two points during ontogeny. I will supply a distance matrix based on Procrustes distances calculated outside of vegan. I have not found an example of a design such as this for adonis. However, I have designed my factors

If one is equal to the reverse of another, keep only one of the pair. B. > On Mar 29, 2018, at 9:48 PM, Ranjan Maitra <maitra at email.com> wrote: > > Dear friends, > > I would like to get all possible arrangements of n objects listed 1:n on a circle. > > Now this is easy to do in R. Keep the last spot fixed at n and fill in the rest using permuations(n-1, n-1)

Thanks! Yes, however, this seems a bit wasteful. Just wondering if there are other, more efficient options possible. Best wishes, Ranjan On Thu, 29 Mar 2018 22:20:19 -0400 Boris Steipe <boris.steipe at utoronto.ca> wrote: > If one is equal to the reverse of another, keep only one of the pair. > > B. > > > > > On Mar 29, 2018, at 9:48 PM, Ranjan Maitra

...ed) I consider to add new instructions, instead of intrinsic, to LLVM. However, there are two options. In the vector LLVM extension, there are dedicated instructions manipulating the vectors like 'extract', 'combine', and 'permute'. DSP and other scientific programs do not permuate the vectors as frequent as 3D programs do. Almost each 3D instruction requires to permuate its operands. For example: // Each register is a 4-component vector // the names of the components are x, y, z, w add r0.xy, r1.zxyw, r2.yyyy The components of r1 and r2 and permuted before the additi...

I don't know if this is more efficient than enumerating with distinct directions and weeding... it seems kind of heavyweight to me: ####### library(gtools) directionless_circular_permutations <- function( n ) { v <- seq.int( n-1 ) ix <- combinations( n-1, 2 ) jx <- permutations( n-3, n-3 ) x <- lapply( seq.int( nrow( ix ) ) , function( i ) {

Hi, This has been asked before but not sufficiently answered from what I could find. How do you create combinations with repetitions (multisets) in R? If I have > set <- array(1:3) And I want to choose all combinations of picking 2 numbers, I want to get a print out like [,1] [,2] [1,] 1 1 [2,] 1 2 [3,] 1 3 [4,] 2 2 [5,] 2 3 [6,] 3 3 subsets(set,

Jeff, I wanted to let you know that your function is faster than generating the directional circular permutations and weeding. Here is the time for n = 10. I compared with just doing the permutations, there is no point in proceeding further with the weeding since it is slower at the start itself. system.time(directionless_circular_permutations(10)) user system elapsed 1.576 0.000

...ably stay intrinsics. In general, intrinsics are far easier to add than instructions. > In the vector LLVM extension, there are dedicated instructions > manipulating the vectors like 'extract', 'combine', and 'permute'. DSP > and other scientific programs do not permuate the vectors as frequent > as 3D programs do. Yes, I think that rob is interested in porting these instructions to mainline LLVM, he just hasn't had time so far. > Almost each 3D instruction requires to permuate its > operands. For example: > > // Each register is a 4-componen...

...should be straight-forward. Take a look at llvm/include/llvm/IntrinsicsPowerPC.td for examples. Adding an intrinsic to LLVM now is just a matter of adding it to the include/llvm/Intrinsics*.td file and adding a line to your code generator .td file. > DSP and other scientific programs do not permuate the vectors as > frequent as 3D programs do. Almost each 3D instruction requires to > permuate its operands. For example: > > // Each register is a 4-component vector > // the names of the components are x, y, z, w > add r0.xy, r1.zxyw, r2.yyyy > > The components of r1...

New function below is a bit faster due to more efficent memory handling. for-loop FTW! directionless_circular_permutations2 <- function( n ) { n1 <- n - 1L v <- seq.int( n1 ) ix <- combinations( n1, 2L ) jx <- permutations( n-3L, n-3L ) jxrows <- nrow( jx ) jxoffsets <- seq.int( jxrows ) result <- matrix( n, nrow = factorial( n1 )/2L, ncol = n ) k

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Buenas tardes amigos, De nuevo por aqui con un incoveniente, tengo el siguiente arreglo: > MuestraS [1] 1 0 0 0 1 0 1 1 1 1 1 0 Deseo realizar todas las permutaciones posibles para luego tomar una muestra aleatoria pequeña, esto lo debo hacer varias veces incrementando el largo del arreglo "MuestraS". El inconveniente esta en que al hacer las permutaciones con este arreglo de 12

I am new to R and I suspect my problem is easily solved, but I haven't been able to figure it out without using loops. I am trying to implement Blair & Karniski's (1993) permutation test. I've included a sample data frame below. This data frame represents the conditional means (C1, C2) for 3 subjects in 2 consecutive samples of a continuous data set (e.g. ERP waveform).

I have a, no doubt, simple question. I wish to write a function such that a <- 9 b <- 10 changer _ function(x,y) { if (y>x){ x <<- Y+1}} Of course there are easier ways to accomplish the task above, but I am more interested in how to have the "x <<- Y+1" part of the function to change x in place for purposes of a much larger function. I have been wrestling with

Sorry for posting this again, but it seems to have become attached to another thread. Guess I replied to another message instead of starting a new one... Hi, I'm trying to setup a call forwarding rule so that when an extention doesn't answer the call is forwarded to my mobile. I'm using voiptalk.org for incoming and outgoing calls and SIP phones for extentions (so all IP based -

Hello, I was wondering if anyone knew how to fit a series using a Garch-M (Garch in the mean) model. From what I gathered from the documentation, it does not seem to be implemented in either fGarch, fSeries, or tSeries. Perhaps there is an option that allows this functionality. Otherwise, if it's possible to modify an existing function I would appreciate any guidance on how to go about

Yes, you're right. In fact, it's just an adaptation of a matlab command and the author advises using N^4 replications that's why it's the default in the function. The bistochastic matrix is not my subject of interest, but I need it to perform some random tranformation of a vector of incomes. Florent Bresson ----- Message d'origine ---- De : Richard M. Heiberger <rmh at

Thanks, I tried someting like this, but computation takes times for large matrices btransf <- function(y,X=length(y)^4) { N <- length(y) bm <- matrix(rep(1/N,N^2),N,N) for(j in 1:X){ coord <- sample(1:N,4,replace=T) d <- runif(1,0,min(bm[coord[1],coord[2]],bm[coord[3],coord[4]]))

Machine is running x86 snv_94 after recent upgrade from opensolaris 2008.05. ZFS and zpool reported no troubles except suggesting upgrade for from ver.10 to ver.11. seemed like a good idea at the time. system up for several days after that point then took down for some unrelated maintenance. now will not boot the opensol, drops to grub prompt, no menus. zfs was mirrored on two disks c6d0s0 and